Fix: Missing Metrics During No Scaling (ActionNoChange)

Have you ever encountered a situation where your autoscaling metrics seem to disappear during periods of stability? This article dives deep into a specific issue where metrics are not emitted when no scaling action is required, a scenario known as ActionNoChange. We'll explore the problem, its impact, root cause, and solution. This issue particularly affects external autoscalers, monitoring systems, and overall operational visibility, so understanding it is crucial for maintaining a robust and reliable system.

Understanding the Problem: The Case of the Missing Metrics

In capacity-only mode, the system is designed to adjust resources based on demand. However, a peculiar issue arises when all variants are operating at their optimal replica count. In such scenarios, where no scaling action is deemed necessary, the system fails to emit metrics to external systems. This creates a significant gap in metric continuity, which can have cascading effects on various components that rely on these metrics. Imagine your monitoring dashboards suddenly flatlining during peak stability – that's the kind of problem we're addressing. The absence of these metrics can lead to misinterpretations by external autoscalers, trigger false alerts, and generally obscure the true state of the system. Therefore, ensuring continuous metric emission, even during stable periods, is essential for maintaining accurate monitoring and effective autoscaling.

The Impact: A Cascade of Consequences

The absence of metric emission during stable periods has a wide range of consequences, affecting various aspects of system monitoring, autoscaling, and overall operational visibility. Let's delve into the specific impacts:

External Autoscalers (HPA)

External autoscalers, such as Horizontal Pod Autoscaler (HPA), rely on a continuous stream of metrics to make informed scaling decisions. When metrics are not emitted during ActionNoChange, these autoscalers may resort to using stale metrics or, worse, interpret the missing data as a failure signal. This can lead to incorrect scaling actions, either scaling up unnecessarily or failing to scale down when demand decreases. The result is inefficient resource utilization and potential performance bottlenecks. To ensure autoscalers function correctly, consistent metric emission is essential.

Monitoring and Observability

Gaps in metric time series are a nightmare for monitoring and observability. These gaps can trigger false alerts, masking genuine issues and leading to alert fatigue. Imagine receiving a critical alert only to find out it was triggered by a missing metric, not an actual problem. Moreover, intermittent metric emission makes it difficult to establish baseline performance and identify anomalies. A continuous flow of metrics provides a clear picture of system behavior, enabling proactive issue detection and resolution. Without it, troubleshooting becomes a guessing game.

Metric Staleness

During stable periods, the last emitted metric becomes increasingly stale. This means that the available data may no longer accurately reflect the current state of the system. Relying on stale metrics for decision-making can lead to suboptimal scaling actions and inaccurate monitoring. For example, if the load gradually increases during a stable period, the stale metrics might not reflect this change, preventing the system from scaling up in time to meet the growing demand. Regular metric emission ensures that the data remains fresh and relevant.

Operational Visibility

Perhaps one of the most critical impacts is the loss of operational visibility. Without continuous metric emission, it becomes impossible to distinguish between a truly stable system and a controller that is not running. This ambiguity can lead to significant challenges in diagnosing issues and ensuring system health. For instance, if metrics suddenly stop flowing, is it because the system is perfectly stable, or has the controller crashed? This lack of clarity can delay incident response and increase the risk of outages. Consistent metric emission provides the necessary transparency to understand the system's state and react promptly to any anomalies.

Identifying the Affected Metrics

Several key metrics are affected by this issue, leading to a diminished view of the system's performance and stability. Understanding which metrics are impacted is crucial for diagnosing the problem and implementing an effective solution. The primary metrics that are not emitted during stable periods include:

inferno_desired_replicas

This metric represents the desired number of replicas for each variant. It is a critical indicator of the system's scaling decisions and its ability to adapt to changing demand. When this metric is not emitted, it becomes difficult to track the system's scaling behavior and ensure that it is aligning with the actual load.

inferno_current_replicas

This metric reflects the current number of replicas running for each variant. It provides a real-time view of the system's resource allocation and its capacity to handle incoming requests. Without this metric, it is challenging to monitor the system's actual state and identify any discrepancies between desired and actual replica counts.

inferno_desired_ratio

This metric calculates the ratio of desired to current replicas. It offers a normalized view of the system's scaling needs, making it easier to compare the scaling behavior of different variants. The absence of this metric can hinder the ability to fine-tune scaling policies and optimize resource utilization.

The lack of these metrics during stable periods creates a blind spot in the system's monitoring landscape, making it harder to detect anomalies, troubleshoot issues, and ensure optimal performance. Therefore, addressing this issue is essential for maintaining a comprehensive and reliable monitoring system.

Reproducing the Issue: A Step-by-Step Guide

To fully grasp the issue of missing metrics during stable periods, it's helpful to reproduce the problem in a controlled environment. This step-by-step guide will walk you through the process, allowing you to observe the behavior firsthand. By replicating the issue, you'll gain a deeper understanding of its impact and the importance of the fix.

Step 1: Deploy a VariantAutoscaling Resource

Begin by deploying a VariantAutoscaling resource with a model that exhibits stable load. This setup simulates a real-world scenario where the system operates at a consistent level of demand. A stable load is crucial for triggering the ActionNoChange condition, which is the focus of this issue.

Step 2: Allow the System to Reach Steady State

Give the system time to reach a steady state, where the replica count has stabilized at its optimal value. This means that the system has scaled up or down as necessary to match the current demand and is now running at its most efficient configuration. This stabilization period is essential for the issue to manifest.

Step 3: Observe Controller Logs

Monitor the controller logs for messages indicating "No scaling decisions to apply." This message confirms that the system has entered the ActionNoChange state, where no scaling actions are being performed. It also signals that metrics might not be emitted during this period.

Expected Log Output During Stable Period:

INFO No scaling decisions to apply

Step 4: Query Prometheus for Metrics

Use Prometheus or your preferred monitoring tool to query the inferno_desired_replicas metric. This metric is one of the key indicators affected by the issue. By querying Prometheus, you can check the timestamps of the metric data points.

Step 5: Note Stale Metric Timestamps

Observe that the metric timestamps are stale, meaning there are no new data points during the stable period. This confirms that metrics are not being emitted when the system is in the ActionNoChange state. The absence of recent timestamps highlights the gap in metric continuity.

Missing (Expected) Log Output:

INFO Successfully emitted metrics for external autoscalers: variant=..., targetReplicas=...

By following these steps, you can clearly observe the issue of missing metrics during stable periods. This hands-on experience will reinforce your understanding of the problem and its implications.

Root Cause Analysis: Tracing the Code Path

To effectively address any issue, it's crucial to understand its root cause. In the case of missing metrics during ActionNoChange, the problem lies within the code path that determines when and how metrics are emitted. By tracing the execution flow, we can pinpoint the exact location where the issue originates. Let's dive into the code and unravel the mystery.

Entry Point: Reconcile() in variantautoscaling_controller.go

The journey begins with the Reconcile() function in the variantautoscaling_controller.go file. This is the primary entry point for the controller's reconciliation loop, which is responsible for ensuring that the system's state aligns with the desired configuration. Understanding how the reconciliation loop functions is essential for grasping the overall control flow.

Capacity Analysis: runCapacityAnalysis()

Within the Reconcile() function, the runCapacityAnalysis() function is invoked. This function performs the crucial task of analyzing the system's capacity and determining the necessary scaling actions. It returns a capacityTargets map, which specifies the desired replica count for each variant based on the current load. This map serves as the foundation for subsequent decision-making.

Decision Conversion: convertCapacityTargetsToDecisions()

The next key step is the convertCapacityTargetsToDecisions() function, located around line 514 in variantautoscaling_controller.go. This function iterates over the capacityTargets map and compares the desired replica count (targetReplicas) with the current replica count (state.CurrentReplicas) for each variant. This comparison is the heart of the decision-making process.

The Bug: Skipping Decision Creation

Here's where the problem lies. Within the convertCapacityTargetsToDecisions() function, specifically around line 545, a crucial check is performed: if targetReplicas is equal to state.CurrentReplicas, the code executes a continue statement. This seemingly innocuous statement is the root cause of the issue. When continue is executed, the code skips the creation of a scaling decision for that variant. This means that no action, including metric emission, is triggered for variants that are already at their optimal replica count.

Decision Application: Line 409

Moving further down the code path, we reach line 409, where the applyCapacityDecisions() function is conditionally called. The condition is if len(allDecisions) > 0. Since the ActionNoChange scenario results in no decisions being added to the allDecisions list, this condition evaluates to false. As a result, the applyCapacityDecisions() function is never called. This is the final nail in the coffin for metric emission during stable periods.

Metric Emission

The applyCapacityDecisions() function is responsible for emitting metrics to external systems. Because it is not called when allDecisions is empty, no metrics are emitted during ActionNoChange. This completes the code path analysis and clearly illustrates how the continue statement in convertCapacityTargetsToDecisions() leads to the issue of missing metrics.

Key Code References

• internal/controller/variantautoscaling_controller.go:543-546 - ActionNoChange skip
• internal/controller/variantautoscaling_controller.go:409-417 - Decision application gate
• internal/controller/variantautoscaling_controller.go:820-828 - Metric emission in applyCapacityDecisions

The Solution: Ensuring Continuous Metric Emission

The solution to the missing metrics issue lies in modifying the code to ensure that metrics are emitted even when no scaling action is required. This involves revisiting the decision-making process and decoupling metric emission from scaling actions. Let's explore the proposed fix and how it addresses the root cause.

The core of the solution involves removing the continue statement that skips decision creation during ActionNoChange. Instead of skipping, a decision with ActionNoChange should be added to the allDecisions list. This ensures that the applyCapacityDecisions() function is always called, regardless of whether scaling actions are needed.

Specifically, the modification should be made in the convertCapacityTargetsToDecisions() function, around line 545 in variantautoscaling_controller.go. The continue statement should be removed, and the code should proceed to create a decision with ActionNoChange. This decision will then be added to the allDecisions list.

With this change, the applyCapacityDecisions() function will be called even during stable periods, triggering metric emission. This ensures a continuous flow of metrics, providing accurate and up-to-date information to external autoscalers, monitoring systems, and operational dashboards.

By implementing this solution, the system will provide a more complete and reliable view of its state, regardless of scaling activity. This enhances the overall robustness and observability of the system.

Conclusion: Maintaining Metric Continuity

The issue of missing metrics during ActionNoChange highlights the importance of continuous metric emission in autoscaling systems. By understanding the problem, its impact, root cause, and solution, we can ensure that our systems provide a comprehensive and accurate view of their state, even during stable periods. The proposed fix, which involves removing the conditional skip in decision creation, ensures that metrics are emitted consistently, enhancing the reliability and observability of the system.

Maintaining metric continuity is crucial for effective autoscaling, monitoring, and operational visibility. By addressing this issue, we can build more robust and resilient systems that adapt seamlessly to changing demands. Remember, a complete picture is essential for making informed decisions and ensuring optimal performance.

For more information on autoscaling and metric monitoring, check out Kubernetes Horizontal Pod Autoscaler.